def test_bin_samples():
samples = range(6)
numpy.random.shuffle(samples)
counts, bounds = bin_samples(samples, 2)
assert_list_equal(list(counts), [3, 3])
assert_list_equal(list(bounds[0]), [0, 3])
assert_list_equal(list(bounds[1]), [3, 5])
def test_bin_samples():
samples = range(6)
numpy.random.shuffle(samples)
counts, bounds = bin_samples(samples, 2)
assert_list_equal(list(counts), [3, 3])
assert_list_equal(list(bounds[0]), [0, 3])
assert_list_equal(list(bounds[1]), [3, 5])
def check_nich(impl, data_count, mean, std):
check_cm(impl)
ss = None
if data_count:
data = np.random.normal(mean, std, size=data_count)
ss = {'count': data_count, 'mean': data.mean(), 'variance': data.var()}
cm = ComponentModel(impl, ss=ss)
samples = cm.sample_data(SAMPS)
counts, bin_ranges = bin_samples(samples)
#use of quadrature is unfortunate but for now
#it's the easiest way to score bins and seems to work
pdf = lambda x: np.exp(cm.pred_prob(x))
probs = [quad(pdf, m, M, epsabs=0., epsrel=1e-6)[0] for m, M in bin_ranges]
assert_less(1 - sum(probs), THRESH)
probs, counts = zip(*sorted(zip(probs, counts), reverse=True)[:TOPN])
p = mgof(probs, counts, SAMPS, truncated=True)
assert_greater(p, THRESH)
def check_nich(impl, data_count, mean, std):
check_cm(impl)
ss = None
if data_count:
data = np.random.normal(mean, std, size=data_count)
ss = {
'count': data_count,
'mean': data.mean(),
'variance': data.var()
}
cm = ComponentModel(impl, ss=ss)
samples = cm.sample_data(SAMPS)
counts, bin_ranges = bin_samples(samples)
#use of quadrature is unfortunate but for now
#it's the easiest way to score bins and seems to work
pdf = lambda x: np.exp(cm.pred_prob(x))
probs = [quad(pdf, m, M, epsabs=0., epsrel=1e-6)[0] for m, M in bin_ranges]
assert_less(1 - sum(probs), THRESH)
probs, counts = zip(*sorted(zip(probs, counts), reverse=True)[:TOPN])
p = mgof(probs, counts, SAMPS, truncated=True)
assert_greater(p, THRESH)
